We compare the performance of our radar-based global descriptor with a hand-crafted ScanContext [1]} method and our re-implementation of the VGG-16/ NetVLAD architecture used as a baseline in [2]}.
Evaluation results are shown in Tab. REF .
Our method (RadarLoc) consistently outperforms other approaches on all evaluation sets at both 5m and 10m threshold.
Recall@1 with 5m threshold is between 2-14 p.p. higher than the runner-up ScanContext.
At larger 10m threshold, learning-based VGG-16/NetVLAD architecture scores higher than ScanContext, but it's still about 5-10 p.p. lower than RadarLoc.
Our method generalizes well to a different dataset. The model trained on a subset of Sejong traversal, from MulRan dataset, has a top performance when evaluated on Radar RobotCar.
It must be noted that these two datasets are acquired using a different suite of sensors, although having similar operational characteristics.
Figure REF  shows Recall@\(N\)  plots, for \(N\)  ranging from 1 to 10, of all evaluated methods on different evaluation sets from MulRan dataset.
For Sejong and KAIST trajectory, the performance of our method increases with \(N\) , quickly reaching near-100% accuracy.
However characteristics of the environment in which data was gathered impacts all evaluated methods.
The results for all traversals acquired in the city centre areas (Sejong, KAIST, Radar RobotCar) are relatively better, for all evaluated methods.
Riverside traversal is acquired outside the city, where significantly fewer structural elements, such as buildings or lamp posts, are present. Hence it gives considerably worse results for all evaluated methods.
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